? CANCER IMAGING & EARLY DETECTION PROGRAM The goal of the Cancer Imaging and Early Detection Program is to advance cancer research, early cancer detection, and cancer management by carrying out novel research using multimodality anatomical and molecular imaging (MI) strategies and in vitro diagnostics. This goal will be achieved through the development and application of multimodality imaging strategies to reveal the molecular basis of cancer, to develop multiparametric diagnostic tools and to advance the effective treatment for cancer. The program is comprised of investigators from eight specialty areas: imaging instrumentation/engineering; modeling/biostatistics; chemistry; molecular imaging; in vitro diagnostics; cancer biology/proteomics; mouse models/small animal imaging applications in cancer therapy; and clinical oncology. Research by program members has resulted in new tools for imaging and new insights into cancer biology and responses to therapy. Exciting advances include the development of new clinical imaging instrumentation at the interface of radiology and pathology; methods to image targeted therapies; next-generation probes for imaging apoptosis/angiogenesis; innovative endoscopic optical imaging strategies; cell sorting devices for in vitro diagnostics; advances in instrumentation for small animal imaging; and direct application of clinical cancer imaging strategies. The Stanford Cancer Institute (SCI) has assisted the program by facilitating recruitments, enhancing the instrumentation for small animal imaging, and providing resources for collaborative projects. The program adds value to the SCI by bringing biologists, chemists, engineers, radiologists, computational scientists and clinical and translational researchers together to solve the challenging problems in imaging and address unmet needs in oncology. Co-led by Sanjiv Sam Gambhir, MD, PhD, and Christopher H. Contag, PhD, the 36 program members come from 10 departments in the Schools of Medicine, Engineering, and Humanities & Sciences. Members are major participants in one P50 program project (ICMIC), one U54 project (CCNE), two U01 projects (EDRN, ICBP), two NCI T32 and one R25T funded postdoctoral training grants. The members are also actively engaged with other cancer centers around the country including the MD Anderson Cancer Center and are participating in clinical trials that involve centers beyond the USA (e.g., South Africa, Korea, China, and India). The leadership is united in its goals for program development to advance cancer imaging. The program has $11.3M in extramural funding (total costs) of which $8.9M is from the NCI, $1.5M from other NIH institutes. Since 2009, program investigators have published over 370 manuscripts relevant to cancer biology in peer-reviewed journals with 35% intra-programmatic, 31% inter-programmatic and numerous externally collaborative manuscripts.

Agency
National Institute of Health (NIH)
Institute
National Cancer Institute (NCI)
Type
Center Core Grants (P30)
Project #
5P30CA124435-13
Application #
9936162
Study Section
Special Emphasis Panel (ZCA1)
Project Start
Project End
Budget Start
2020-06-01
Budget End
2021-05-31
Support Year
13
Fiscal Year
2020
Total Cost
Indirect Cost
Name
Stanford University
Department
Type
DUNS #
009214214
City
Stanford
State
CA
Country
United States
Zip Code
94305
Patel, Manali I; Sundaram, Vandana; Desai, Manisha et al. (2018) Effect of a Lay Health Worker Intervention on Goals-of-Care Documentation and on Health Care Use, Costs, and Satisfaction Among Patients With Cancer: A Randomized Clinical Trial. JAMA Oncol 4:1359-1366
Trieu, Vanessa; Pinto, Harlan; Riess, Jonathan W et al. (2018) Weekly Docetaxel, Cisplatin, and Cetuximab in Palliative Treatment of Patients with Squamous Cell Carcinoma of the Head and Neck. Oncologist 23:764-e86
Kuonen, François; Surbeck, Isabelle; Sarin, Kavita Y et al. (2018) TGF?, Fibronectin and Integrin ?5?1 Promote Invasion in Basal Cell Carcinoma. J Invest Dermatol 138:2432-2442
Gee, Marvin H; Han, Arnold; Lofgren, Shane M et al. (2018) Antigen Identification for Orphan T Cell Receptors Expressed on Tumor-Infiltrating Lymphocytes. Cell 172:549-563.e16
Malta, Tathiane M; Sokolov, Artem; Gentles, Andrew J et al. (2018) Machine Learning Identifies Stemness Features Associated with Oncogenic Dedifferentiation. Cell 173:338-354.e15
Banerjee, Imon; Gensheimer, Michael Francis; Wood, Douglas J et al. (2018) Probabilistic Prognostic Estimates of Survival in Metastatic Cancer Patients (PPES-Met) Utilizing Free-Text Clinical Narratives. Sci Rep 8:10037
Thorsson, Vésteinn; Gibbs, David L; Brown, Scott D et al. (2018) The Immune Landscape of Cancer. Immunity 48:812-830.e14
Rogers, Zoë N; McFarland, Christopher D; Winters, Ian P et al. (2018) Mapping the in vivo fitness landscape of lung adenocarcinoma tumor suppression in mice. Nat Genet 50:483-486
Nair, Viswam S; Sundaram, Vandana; Desai, Manisha et al. (2018) Accuracy of Models to Identify Lung Nodule Cancer Risk in the National Lung Screening Trial. Am J Respir Crit Care Med 197:1220-1223
She, Richard; Jarosz, Daniel F (2018) Mapping Causal Variants with Single-Nucleotide Resolution Reveals Biochemical Drivers of Phenotypic Change. Cell 172:478-490.e15

Showing the most recent 10 out of 322 publications